Artificial intelligence as a support tool for providing feedback on coursework in higher education: A pilot study
DOI:
https://doi.org/10.7577/seminar.6616Emneord (Nøkkelord):
academic writing, higher education, ai-generated feedback, feedforward, pedagogical scaffolding, human feedbackSammendrag
The increasing use of artificial intelligence (AI) in higher education is reshaping feedback practices and raising questions about how AI-generated feedback compares with feedback provided by humans. This pilot study investigates the pedagogical potential of an AI assistant for providing feedback[1] on student´ academic writing in higher education. Drawing on a sociocultural perspective on scaffolding and framework distinguishing between feed back and feed forward, the study examines (i) how students experience AI-generated feedback compared with feedback provided by learning assistants, (ii) the extent to which the two feedback sources align with predefined assessment criteria, and (iii) their similarities and differences in the content of the provided feed back and feed forward. Data comprised students´ survey responses and content analysis of feedback provided by the AI assistant and learning assistants on the same type of academic writing assignment. The findings show that both feedback sources largely align with the predefined assessment criteria and provide feedback on students´ current performance as well as feed forward aimed at further development. However, they differ in how this support is enacted. The AI assistant tends to provide more explicit, criteria-focused, and action-oriented guidance, whereas learning assistants provide more contextualized and relationally grounded feedback. Students also expressed a preference for feedback from learning assistants on future assignments. Viewed through a sociocultural lens, these findings suggest that AI-generated and human feedback may represent distinct but complementary forms of pedagogical scaffolding. Rather than replacing human feedback, AI may therefore serve as a pedagogical resource that combines explicit guidance with dialogic and relational support.
[1] In this study, feedback is used as an overarching concept encompassing three complementary functions: feed up (clarifying goals and exptectations), feed back (information about current performance), and feed forward (guidance for future improvement).
Referanser
Berg, B. L., & Lune, H. (2012). Qualitative research methods for the social sciences. Pearson Education Inc.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281. https://doi.org/10.3102/0034654306543065003245
Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31(2), 219–233. https://doi.org/10.1080/03070600572132
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354
Chalmers, C., Mowat, E., & Chapman, M. (2018). Marking and providing feedback face-to-face: Staff and student perspectives. Active Learning in Higher Education, 19(1), 35–45. https://doi.org/10.1177/1469787417721363
Dai, W., Tsai, Y. S., Gašević, D., & Chen, G. (2025). Designing relational feedback: A rapid review and qualitative synthesis. Assessment & Evaluation in Higher Education, 50(1), 16–30. https://doi.org/10.1080/02602938.2024.2361166
de Kleijn, R. A., Mainhard, M. T., Meijer, P. C., Pilot, A., & Brekelmans, M. (2012). Master’s thesis supervision: Relations between perceptions of the supervisor–student relationship, final grade, perceived supervision contribution to learning and student satisfaction. Studies in Higher Education, 37(8), 925–939. https://doi.org/10.1080/03075079.2011.556717
Elander, J., Harrington, K., Norton, L., Robinson, H., & Reddy, P. (2006). Complex skills and academic writing: A review of evidence about the types of learning required to meet core assessment criteria. Assessment & Evaluation in Higher Education, 31(1), 71–90. https://doi.org/10.1080/02602930500262379
Engeness, I., & Mørch, A. (2016). Developing writing skills in English using content-specific computer-generated feedback with EssayCritic. Nordic Journal of Digital Literacy, 11(2), 118–135. https://doi.org/10.18261/issn.1891-943x-2016-02-03
Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: Insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20, Article 57. https://doi.org/10.1186/s41239-023-00425-2
Falchikov, N. (2005). Improving assessment through student involvement. Routledge Falmer.
Fauskanger, J., & Mosvold, R. (2014). Innholdsanalysens muligheter i utdanningsforskning. Norsk pedagogisk tidsskrift, 98(2), 127–139. https://doi.org/10.18261/ISSN1504-2987-2014-02-07
Firing, K., Klomsten, A. T., & Moen, F. (2013). Masterstudenters opplevelse av møter med veileder: «Det er veiledningen som gjør at en føler at en mestrer.» Uniped, 36(2), 81–92. https://doi.org/10.3402/uniped.v36i2.21515
Gentile, M., Città, G., Perna, S., & Allegra, M. (2023). Do we still need teachers? Navigating the paradigm shift of the teacher’s role in the AI era. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1161777
Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/i.ijis.2020.09.001
Guardia-Paniura, C. H., Cueva-Luza, T., Cruz-Carpio, F. M., Ito-Díaz, R. R., Apaza-Paco, D. V., Rosas-Rojas, N., Mamani-Mamani, B., Terrero-Pérez, Á., Yaedú, R. Y. F., & Peralta-Mamani, M. (2026). Human and AI-generated feedback in higher education: A systematic review of effectiveness and student perceptions. Contemporary Educational Technology, 18(1), ep623. https://doi.org/10.30935/cedtech/17863
Guo, K., & Wang, D. (2024). To resist it or to embrace it? Examining ChatGPT’s potential to support teacher feedback in EFL writing. Education and Information Technologies, 29, 8435-8463. https://doi.org/10.1007/s10639-023-12146-0
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Haugan, J., & Lysebo, M. (2015). Medstudentvurdering i matematikk og fysikk. Uniped, 38(4), 327–335. https://doi.org/10.18261/ISSN1893-8981-2015-04-09
Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Huang, J., Shen, G., & Ren, X. (2021). Connotation analysis and paradigm shift of teaching design under artificial intelligence technology. International Journal of Emerging Technologies in Learning (iJET), 16(5), 73–86. https://doi.org/10.3991/ijet.v16i05.20287
Jonsmoen, K. M., & Greek, M. (2012). Skrivekår i profesjonsutdanning. Uniped, 35(4), 4–13. https://doi.org/10.3402/uniped.v35i4.20258
Kaliisa, R., Misiejuk, K., López-Pernas, S. & Saqr, M. (2026). How does artificial intelligence compare to human feedback? A meta-analysis of performance, feedback perception, and learning dispositions. Educational Psychology, 46(1), 80-111. https://10.1080/01443410.2025.2553639
Liu, W. (2024). A systematic review of automated writing evaluation feedback: Validity, effects and students’ engagement. Language Teaching Research Quarterly, 45, 86-105. https://doi.org/10.32038/ltrq.2024.45.05
Mørch, A. I., Engeness, I., Cheng, V. C., Cheung, W. K., & Wong, K. C. (2017). EssayCritic: Writing to learn with a knowledge-based design critiquing system. Journal of Educational Technology & Society, 20(2), 213–223.
Nazaretsky, T., P. Mejia-Domenzain, V. Swamy, J. Frej, and T. Käser. (2026). Who Gives Feedback Matters: Student Biases Towards Human and AI-Generated Formative Feedback. Journal of Computer Assisted Learning 42, no. 1: e70153. https://doi.org/10.1111/jcal.70153.
National Committee for Research Etichs in the Social Sciences and the Humanities (NESH). (2023). Guidelines for research ethics in the social sciences and humanities. https://www.forskningsetikk.no/en/guidelines/social-sciences-and-humanities/guidelines-for-research-ethics-in-the-social-sciences-and-the-humanities/
NOU 2022: 2. (2022). Akademisk ytringsfrihet (Academic freedom of expression). https://www.regjeringen.no/no/dokumenter/nou-2022-2/id2903951/
NTNU. (2026). Strategi – NTNU 2035: Strategi for Norges teknisk naturvitenskapelige universitet. https://www.ntnu.no/ntnus-strategi
Owusu-Agyeman, Y., & Moroeroe, E. M. (2023). Relationality and student engagement in higher education: Towards enhanced students’ learning experiences. International Journal of Emotional Education, 15(2), 37–53. https://doi.org/10.56300/ZANL1419
Parmigiani, D., Nicchia, E., Murgia, E., & Ingersoll, M. (2024). Formative assessment in higher education: An exploratory study within programs for professionals in education. Frontiers in Education, 9, 1366215. https://doi.org/10.3389/feduc.2024.1366215
Price, M., Handley, K., Millar, J., & O’Donovan, B. (2010). Feedback: All that effort, but what is the effect? Assessment & Evaluation in Higher Education, 35(3), 277–289. https://doi.org/10.1080/02602930903541007
Ruegg, R. (2015). Differences in the uptake of peer and teacher feedback. RELC Journal, 46(2), 131–145. https://doi.org/10.1177/0033688214562799
Sadler, I., Reimann, N., & Sambell, K. (2023). Feedforward practices: A systematic review of the literature. Assessment & Evaluation in Higher Education, 48(3), 305–320. https://doi.org/10.1080/02602938.2022.2073434
Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
Sritrakarn, N. (2018). A comparison of teachers’ and senior students' feedback: Student attitudes and their writing improvement. The Journal of Asia TEFL, 15(2), 329–348. https://doi.org/10.18823/asiatefl.2018.15.2.5.329
Tensen, D., Grainger, P., & Graham, W. (2025). Using AI to generate formative feedback in doctoral education. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2025.2536558
Thompson, R., & Lee, M. J. (2012). Talking with students through screencasting: Experimentations with video feedback to improve student learning. The Journal of Interactive Technology & Pedagogy.
Topping, K. (1998). Peer assessment between students in colleges and universities. Review of Educational Research, 68(3), 249–276. https://doi.org/10.3102/00346543068003249
UNICEF. (2021). Policy guidance on AI for children 2.0: Recommendations for building AI policies and systems that uphold child rights. https://www.unicef.org/innocenti/media/1341/file/UNICEF-Global-Insight-policy-guidance-AI-children-2.0-2021.pdf
Urzúa, C. A. C., Ranjan, R., Saavedra, E. E. M., Badilla-Quintana, M. G., Lepe-Martinéz, N., & Philominraj, A. (2025). Effects of AI-assisted feedback via generative chat on academic writing in higher education students: A systematic review of the literature. Education Sciences, 15(10), 1396. https://doi.org/10.3390/educsci15101396
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1706.03762
von Krogh, G. (1998). Care in knowledge creation. California Management Review, 40,(3)133–153. https://doi.org/10.2307/41165947
Värlander, S. (2008). The role of students’ emotions in formal feedback situations. Teaching in Higher Education, 13(2), 145–156. https://doi.org/10.1080/13562510801923195
Vygotsky, L. S. (1978). Mind in Society. The development of Higher Psychological Processes. Harvard University Press.
Wood, D., Bruner, J. S. & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., ... Wen, J.-R. (2026). A survey of large language models. Frontiers of Computer Science, 20, Article 2012627. https://doi.org/10.1007/s11704-026-60308-3
Zhang, K., Li, S., & Huang, Y. T. (2026). Generative AI-driven feedback in higher education: A scoping review. Assessment & Evaluation in Higher Education, 1–18. https://doi.org/10.1080/02602938.2026.2617157
Nedlastinger
Publisert
Hvordan referere
Utgave
Seksjon
Lisens
Opphavsrett 2026 Anne Torhild Klomsten, Vegard M. Frantzen, Mikhail Fominykh

Dette verket er lisensiert under Creative Commons Attribution 4.0 International License.
Seminar.net is a fully open access journal, which means that all articles are available on the internet to all users immediately upon publication. Use and distribution in any medium is permitted, provided the author and the journal are properly credited. The journal allow reuse and remixing of content in accordance with a Creative Commons license CC-BY
- The journal allows the author(s) to hold the copyright without restrictions.
- The journal allows the author(s) to retain publishing rights without restrictions.
- Seminar.net does not charge authors for publishing with us.